Detail-aware discriminative feature learning model for vehicle re-identification

QIU Mingkai; LI Xiying

doi:10.13471/j.cnki.acta.snus.2020.03.16.2020B023

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Detail-aware discriminative feature learning model for vehicle re-identification

Research articles | 更新时间：2023-11-01

- Detail-aware discriminative feature learning model for vehicle re-identification
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 60, Issue 4, Pages: 111-120(2021)
- 作者机构：
  
  中山大学智能工程学院 / 广东省智能交通系统重点实验室 / 视频图像智能分析与应用技术公安部重点实验室，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2020.03.16.2020B023
  CLC： TP183
- Published：25 July 2021，
  
  Published Online：15 September 2020，
  
  Received：16 March 2020，
  
  Accepted：07 May 2020
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邱铭凯,李熙莹.用于车辆重识别的基于细节感知的判别特征学习模型[J].中山大学学报(自然科学版),2021,60(04):111-120.

QIU Mingkai,LI Xiying.Detail-aware discriminative feature learning model for vehicle re-identification[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(04):111-120.
邱铭凯,李熙莹.用于车辆重识别的基于细节感知的判别特征学习模型[J].中山大学学报(自然科学版),2021,60(04):111-120. DOI： 10.13471/j.cnki.acta.snus.2020.03.16.2020B023.

QIU Mingkai,LI Xiying.Detail-aware discriminative feature learning model for vehicle re-identification[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(04):111-120. DOI： 10.13471/j.cnki.acta.snus.2020.03.16.2020B023.

摘要

在多摄像头下拍摄的车辆图片集合中，对属于目标车辆的图片进行匹配为车辆重识别。如何有效的区分具有相似外观的不同车辆的图片，是车辆重识别的一大挑战。考虑到不同车辆之间的差异集中于车窗等区域的细节，文章提出了一个基于细节感知的判别特征学习模型；设计了一个指导式的车辆局部特征提取流程，将局部特征与骨干网络提取的全局特征联合作为车辆的提取特征，不同车辆联合特征之间的欧式距离作为相似度衡量。在算法实验中，所提出的算法在公开数据集VehicleID与VeRi上都取得领先于现有车辆重识别算法的结果，验证了算法的有效性。

Abstract

Vehicle re-identification （Re-ID） aims to identify a target vehicle from multiple non-overlapping cameras. Vehicle Re-ID is a challenging work because it's hard to distinguish vehicles of the same model with similar appearance. Since the differences between these vehicles are concentrated in some small local regions， a detail-aware discriminative feature learning model is proposed in this paper， based on the assumption that features of network's middle layer is helpful in extracting discriminative feature representation of local regions. In the proposed model， a guided vehicle local feature extraction process is designed， and the final feature representation of vehicle consist of the extracted local feature and the global feature extracted by the backbone network. Extensive experiments over benchmark datasets VehicleID and VeRi have shown that the proposed methods could achieve superior performance than state-of-the-art methods.

关键词

车辆重识别神经网络局部特征提取判别特征学习

Keywords

vehicle re-identificationneural networklocal feature extractiondiscriminative feature learning

references

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